A learning system is adapted to provide a learning management system adapted to present an exercise environment to the user in which the user is able to practice skills through a combination of presented media and practice areas. The learning system is adapted to provide feedback to the user during the practice session to allow the user to improve performance while in the exercise environment. Additionally, the learning system is adapted to track the user's performance in terms of practice time and in terms of accuracy percentage to allow the student and the educator to monitor performance related to effort. Still additionally, the learning systems of the present disclosure may be adapted to track the types of errors committed by the users to enable the users and educators to provide more directed educational experiences and practice sessions to overcome recurring problems.
Legal claims defining the scope of protection, as filed with the USPTO.
1. A network-based learning system, comprising: an input module configured to receive psychomotor input, wherein the psychomotor input comprises English words translated by a software application, wherein the software application is configured to convert machine steno into English; and an analysis module configured to compare the received psychomotor input with ideal psychomotor input.
2. The network-based learning system of claim 1 , wherein the analysis module compares received psychomotor input on a word-by-word basis as a user is generating the psychomotor input.
3. The network-based learning system of claim 1 , wherein the analysis module compares received psychomotor input after a user has finished generating the psychomotor input.
4. The network-based learning system of claim 1 , wherein the analysis module is adapted to present focused repractice to a user, wherein the focused repractice comprises repractice of specific errors by at least one of a word, a phrase, and an error type.
5. The network-based learning system of claim 1 , wherein the analysis module displays differences between the psychomotor input and the ideal psychomotor input through at least one of highlighted text, bolded text, strikethrough text, text of differing colors, text of differing sizes, and italicized text.
6. The network-based learning system of claim 1 , wherein the analysis module displays a summary to a user comprising kinds of errors, wherein the kinds of errors further comprises at least one of drops, untranslates, and wrong words.
7. The network-based learning system of claim 6 , wherein the term “drops” comprises omitted words.
8. The network-based learning system of claim 6 , wherein the term “untranslates” comprises words that the software application did not translate into English text.
9. The network-based learning system of claim 6 , wherein the phrase “wrong words” comprises words from a user's psychomotor input that do not match words from the ideal psychomotor input.
10. The network-based learning system of claim 1 , wherein the analysis module displays a summary to a user that includes a rate of an occurrence of errors.
11. The network-based learning system of claim 1 , wherein a communication module communicates the psychomotor input to a data module as psychomotor performance data, wherein the psychomotor performance data includes at least one of errors, kinds of errors, rates of occurrence of errors, percent accuracy, and time spent, and wherein the data module is configured to store the psychomotor performance data.
12. The network-based learning system of claim 1 , further comprising a reporting module configured to display the psychomotor performance data, through at least one of a fable, a bar graph, a line graph, a pie chart, a calendar, a Stacked bar graph, and as scatterplot diagram.
13. A network-based learning system, comprising: an input module configured to receive psychomotor input, wherein the psychomotor input comprises English words translated by a software application, wherein the software application is configured to convert machine steno input into English text; and an analysis module configured to compare the received psychomotor input with ideal psychomotor input, wherein the analysis module is configured to display differences between the psychomotor input and the ideal psychomotor input, wherein the differences are displayed via at least one of highlighted text, bolded text, strikethrough text, text of differing colors, text of differing sizes, and italicized text.
14. The network-based learning system of claim 13 , wherein the analysis module is configured to compare the received psychomotor input on a word-byword basis as a user is generating the psychomotor input.
15. The network-based learning system of claim 13 , wherein the analysis module compares the received psychomotor input after a user has finished generating the psychomotor input.
16. The network-based learning system of claim 13 , wherein the analysis module is adapted to present focused repractice to a user, wherein the focused repractice comprises repractice of specific errors by at least one of a word, a phrase, and an error type.
17. The network-based learning system of claim 13 , wherein the analysis module displays differences between the psychomotor input and the ideal psychomotor input through at least one of highlighted text, bolded text, strikethrough text, text of differing colors, text of differing sizes, and italicized text.
18. The network-based learning system of claim 13 , wherein the analysis module displays a summary to a user that comprises kinds of errors, wherein the kinds of errors further comprises at least one of drops, untranslates, and wrong words.
19. A network-based learning system, comprising: an input module configured to receive psychomotor input, wherein the psychomotor input comprises English words translated by a software application wherein the software application is configured to convert machine steno into English; an analysis module configured to compare the received psychomotor input with ideal psychomotor input, wherein the analysis module is configures to display differences between the psychomotor input and the ideal psychomotor input through at least one of highlighted text, bolded text, strikethrough text, text of differing colors, text of differing sizes, and italicized text, and wherein the analysis module displays a summary to the user that includes kinds of errors, wherein the kinds of errors comprise at least one of omitted words, words that the software application did not translate into English, and words from the user's psychomotor input that do not match words from the ideal psychomotor input.
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December 14, 2012
April 8, 2014
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